docstr: Compute all seasonality components.
  Parameters
  ----------
      f_r : torch.Tensor, float
          future regressors inputs
      mode: string, either "additive" or "multiplicative"
          mode of the regressors
  Returns
  -------
      torch.Tensor
          Forecast component of dims (batch, n_forecasts, no_quantiles)
docstr_tok: ["Compute","all","seasonality","components",".","Parameters","--","--","--","--","--","f_r",":","torch.Tensor",",","float","future","regressors","inputs","mode",":","string",",","either","``","additive","''","or","``","multiplicative","''","mode","of","the","regressors","Returns","--","--","--","-","torch.Tensor","Forecast","component","of","dims","(","batch",",","n_forecasts",",","no_quantiles",")"]
code: def forward(self, inputs, mode, indeces=None):
      """Compute all seasonality components.
      Parameters
      ----------
          f_r : torch.Tensor, float
              future regressors inputs
          mode: string, either "additive" or "multiplicative"
              mode of the regressors
      Returns
      -------
          torch.Tensor
              Forecast component of dims (batch, n_forecasts, no_quantiles)
      """
  
      if "additive" == mode:
          f_r = self.scalar_features_effects(
              inputs, self.regressor_params["additive"], indeces
          )
      if "multiplicative" == mode:
          f_r = self.scalar_features_effects(
              inputs, self.regressor_params["multiplicative"], indeces
          )
      return f_r
  
file_path: neural_prophet/neuralprophet/components/future_regressors/linear.py
imports: import torch
  import torch.nn
  from neuralprophet.components.future_regressors import FutureRegressors
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 4 for calling the LinearFutureRegressors obj's underlying member method code with example usage: obj.forward(inputs, mode, indeces) and returns: f_r